Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_0pnt2_bert_ft_cola-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_0pnt2_bert_ft_cola-32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-32") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Added training logs
1741bf7 -
Saving weights and logs of step 1603
eb0a5a0 -
Saving weights and logs of step 1600
36918e1 -
Saving weights and logs of step 1500
60bc1c0 -
Saving weights and logs of step 1400
6907bb4 -
Saving weights and logs of step 1300
b70470f -
Saving weights and logs of step 1200
f59859d -
Saving weights and logs of step 1100
ec001ca -
Saving weights and logs of step 1000
66a5e72 -
Saving weights and logs of step 900
f0a9046 -
Saving weights and logs of step 800
de04692 -
Saving weights and logs of step 700
d959e78 -
Saving weights and logs of step 600
6603eda -
Saving weights and logs of step 500
01e7f71 -
Saving weights and logs of step 400
d0b0060 -
Saving weights and logs of step 300
a77ae9e -
Saving weights and logs of step 200
5f7af49 -
Saving weights and logs of step 100
78ffee3 -
Saving weights and logs of step 0
56a915b -
Saving tokenizer
a43029e -
initial commit
0d4be12